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loading conditions. By generating datasets from finite element simulations, ML models can learn the mapping between unit cell design parameters and homogenised properties. State-of-the-art approaches
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or physics. It is a condition of employment that the doctoral degree has been awarded. Experience from the finite-element method is a requirement. Experience from computational structural dynamics is a
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candidate will enjoy working on finite-element based modelling, the application of mathematical concepts from UQ/ML to practical problems, and an understanding of scripting/programming. Individuals with
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certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming
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both diffusive and nondiffusive simulations including hydrodynamic transport effects with a Finite Element solver. The final goal is to identify deviations from macroscopic diffusion in a range of
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discovering research and potentially pursuing a PhD. Expected skills • Solid background in numerical methods (PDEs, finite elements, scientific computing). • Interest in modeling, model order reduction, and
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department at TU/e for finite element-based deformable body simulations. Conduct research on mechanical contact processing models, integrating both physics-based numerical models and data-driven approaches
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Strong background in computational science, applied mathematics, or computational biology Ideally, familiarity with numerical methods for PDEs (e.g., finite difference, finite element) HPC experience is a
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) transport; • Are familiar with chemical simulation techniques, including but not limited to density functional theory, molecular dynamics, (kinetic) Monte Carlo modeling, finite-element modeling, and multi
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related discipline. This project would suit a candidate with a background in mechanical, control or aerospace engineering, physics, mathematics, or other relevant engineering/science degree. The ideal